A fully embedded two-stage coder for hyperspectral near-lossless compression

This letter proposes a near-lossless coder for hyperspectral images. The coding technique is fully embedded and minimizes the distortion in the l2 norm initially and in the l∞ norm subsequently. Based on a two-stage near-lossless compression scheme, it includes a lossy and a near-lossless layer. The novelties are: the observation of the convergence of the entropy of the residuals in the original domain and in the spectral-spatial transformed domain; and an embedded near-lossless layer. These contributions enable a progressive transmission while optimising both SNR and PAE performance. The embeddedness is accomplished by bitplane encoding plus arithmetic encoding. Experimental results suggest that the proposed method yields a highly competitive coding performance for hyperspectral images, outperforming multi-component JPEG2000 for l∞ norm and pairing its performance for l2 norm, and also outperforming M-CALIC in the near-lossless case -for PAE ≥5-

Almost fixed quality rate-allocation under unequal scaling factors for on-board remote-sensing data compression

This article extends a rate-allocation method based on the near-lossless-rate (NLR) complexity that is designed to operate on-board spacecrafts, to include support for distortion scaling factors, such as those that are needed to code multi- and hyperspectral image when a spectral transform is employed. In this article, the conditions to achieve global minimum distortion are derived under the rate-distortion model based on the NLR complexity for the case of varying distortion scaling factors. Practical implementation issues are dealt with, and a rate-allocation method capable of operating under the constraints of on-board operation is provided. An exhaustive experimental validation of the rate-allocation method is performed, reporting modest performances for low rates and close to optimal performances for high rates

Isorange pairwise orthogonal transform

Spectral transforms are tools commonly employed in multi- and hyperspectral data compression to decorrelate images in the spectral domain. The Pairwise Orthogonal Transform (POT) is one such transform that has been specifically devised for resource-constrained contexts like those found on board satellites or airborne sensors. Combining the POT with a 2D coder yields an efficient compressor for multi- and hyperspectral data. However, a drawback of the original POT is that its dynamic range expansion -i.e., the increase in bit depth of transformed images- is not constant, which may cause problems with hardware implementations. Additionally, the dynamic range expansion is often too large to be compatible with the current 2D standard CCSDS 122.0-B-1. This paper introduces the Isorange Pairwise Orthogonal Transform, a derived transform that has a small and limited dynamic range expansion, compatible with CCSDS 122.0-B-1 in almost all scenarios. Experimental results suggest that the proposed transform achieves lossy coding performance close to that of the original transform. For lossless coding, the original POT and the proposed isorange POT achieve virtually the same performance

Lossy-to-lossless 3D image coding through prior coefficient lookup tables

This paper describes a low-complexity, highefficiency, lossy-to-lossless 3D image coding system. The proposed system is based on a novel probability model for the symbols that are emitted by bitplane coding engines. This probability model uses partially reconstructed coefficients from previous components together with a mathematical framework that captures the statistical behavior of the image. An important aspect of this mathematical framework is its generality, which makes the proposed scheme suitable for different types of 3D images. The main advantages of the proposed scheme are competitive coding performance, low computational load, very low memory requirements, straightforward implementation, and simple adaptation to most sensors

Lossy-to-Lossless Compression of Hyperspectral Imagery Using Three-Dimensional TCE and an Integer KLT

Abstract—An embedded lossy-to-lossless coder for hyperspectral images is presented. The proposed coder couples a reversible, integer-valued Karhunen-Loève transform (KLT) with an extension into 3D of the tarp-based coding with classification for embedding (TCE) algorithm that was originally developed for lossy coding of 2D images. The resulting coder obtains lossy-tolossless operation while closely matching the lossy performance of JPEG2000. Additionally, for lossless compression, it consistently outperforms not only JPEG2000 but often several prominent purely lossless methods. Index Terms—tarp filtering, TCE, integer KLT, hyperspectral compression, lossy-to-lossless codin